1. Field
Embodiments disclosed herein relate to data management. More specifically, embodiments disclosed herein relate to facilitating consistency between a glossary and a repository.
2. Description of the Related Art
Data management is a critical process for any business. Enterprise-level data systems often pay specific attention to key data elements called master data. Master data elements contain high-value business data that is used repeatedly across multiple business process and applications. Name, address, phone number, and date of birth are some common examples of master data associated with customer records.
Master data records are typically synthesized from specific, structured data sources, such as order forms, registration forms, accounting records, and such. These standard sources, while providing key information, capture static data. That is, a customer's name and address are not as fluid or dynamic as customer satisfaction or product enhancements.
Over time, businesses often receive a large quantity of data in unstructured formats that is relevant to master data entries. For example, email correspondence from customers often conveys the customer's level of satisfaction with a product and/or service. These relevant data elements are often ignored because conventional master data models and management systems do not necessarily have the capability to incorporate data from unstructured sources. However, at least in some cases, it is conventionally possible to perform an automated extraction of relevant information from unstructured data, such as through a structured query (e.g., a Structured Query Language (SQL) query). Such extractions are often referred to as data mining.